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+focuses+on+addressing+overlapped+entity+recognition.+Compared+to+Flat+Named+Entity+Recognition+(FNER)%252C+annotated+resources+are+scarce+in+the+corpus+for+NNER.+Data+augmentation+is+an+effective+approach+to+address+the+insufficient+annotated+corpus.+However%252C+there+is+a+significant+lack+of+exploration+in+data+augmentation+methods+for+NNER.+Due+to+the+presence+of+nested+entities+in+NNER%252C+existing+data+augmentation+methods+cannot+be+directly+applied+to+NNER+tasks.+Therefore%252C+in+this+work%252C+we+focus+on+data+augmentation+for+NNER+and+resort+to+more+expressive+structures%252C+Composited-Nested-Label+Classification+(CNLC)+in+which+constituents+are+combined+by+nested-word+and+nested-label%252C+to+model+nested+entities.+The+dataset+is+augmented+using+the+Composited-Nested-Learning+(CNL).+In+addition%252C+we+propose+the+Confidence+Filtering+Mechanism+(CFM)+for+a+more+efficient+selection+of+generated+data.+Experimental+results+demonstrate+that+this+approach+results+in+improvements+in+ACE2004+and+ACE2005+and+alleviates+the+impact+of+sample+imbalance.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.bbc0356c91e5c6a6f201d792c481ca3a%3frik%3dGxr423dlJc6zEg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Nested+Data+for+Deep+Learning&FORM=IRPRST&ck=3985E96CFCD5F38D148643FDF00E4C27&selectedIndex=3&itb=0)
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